IT, Data & Security
Between now and the end of 2027, IT, Data & Security functions will undergo a critical shift. The role moves from operating and securing systems to enabling, governing, and assuring AI-enabled decision-making across the enterprise.
As AI becomes embedded in every core business function, IT and security leaders are no longer supporting the business at the margins. They become central to whether AI delivers value safely, consistently, and at scale.
The result is an IT function that is less focused on infrastructure alone, and more focused on integration, data integrity, and trust.
The 2026-2027 Time Horizon
The changes described here reflect near-term operational reality rather than distant transformation. They are grounded in:
- AI capabilities already embedded in enterprise platforms, data tools, and security solutions
- Increasing use of AI-assisted decision-making in regulated and high-risk environments
- A realistic 18-24 month trajectory as AI moves from experimentation to default operation
By the end of 2027, organisations that have not adapted their IT and security models will struggle to govern AI-driven activity effectively.
Where Most Organisations Are Today
At the start of 2026, IT, Data & Security functions are commonly characterised by:
- Focus on system availability, performance, and cost control
- Data fragmented across applications and departments
- Security models designed primarily for human-driven activity
- AI initiatives managed as isolated projects or pilots
- Limited clarity on accountability for AI-driven decisions
These models are understandable, but increasingly insufficient as AI becomes operational across the enterprise.
Key Transformations
Enterprise Architecture and Integration
By 2027, integration becomes the dominant architectural concern.
AI systems rely on consistent, high-quality data flowing across finance, sales, operations, HR, and external partners. IT teams focus on creating resilient integration layers and shared data foundations rather than point-to-point connections.
Architecture shifts from supporting individual applications to supporting enterprise-wide intelligence.
Data Management and Quality
Data quality moves from a background concern to a board-level issue.
AI systems continuously consume and act on data, making errors and inconsistencies visible immediately. IT and data teams establish stronger ownership, monitoring, and remediation of data quality across the organisation.
Trust in AI outputs becomes inseparable from trust in underlying data.
AI Governance and Control
Governance becomes continuous rather than policy-driven.
By 2027, organisations define clear accountability for:
- Where AI is used in decision-making
- Which decisions are automated, assisted, or prohibited
- How AI behaviour is monitored and adjusted over time
IT and security functions provide the frameworks that allow innovation without loss of control.
Cybersecurity and Risk Management
Security models evolve to address AI-driven activity.
AI increases both defensive capability and attack surface. Security teams use AI to detect anomalies, predict threats, and respond faster - while also protecting AI systems themselves from manipulation, data poisoning, and misuse.
Cybersecurity becomes more proactive, but also more complex.
Compliance, Privacy, and Regulation
Regulatory expectations increase as AI becomes operational.
IT and security leaders ensure that AI usage complies with data protection, privacy, and industry-specific regulation. Explainability, auditability, and traceability become essential characteristics of enterprise systems.
Compliance shifts from documentation to demonstrable control.
What Changes - And What Does Not
What meaningfully changes
- Centrality of IT and security to business decision-making
- Importance of data integration and quality
- Need for explicit AI governance models
- Security posture required to manage AI-driven risk
What does not change
- Accountability for technology risk remains human
- Core principles of security and privacy still apply
- Strong architecture and discipline remain essential
- Trust is earned through consistent control and transparency
AI increases the consequences of poor IT foundations - it does not remove the need for them.
Operating Model Implications
By 2027, IT, Data & Security functions typically:
- Operate as strategic enablers rather than service providers
- Require closer collaboration with business leaders
- Develop new capabilities in data governance, AI oversight, and risk assurance
Roles evolve toward architecture, integration, governance, and security engineering, supported by automation of routine operations.
Questions for Leaders
As AI becomes embedded across the enterprise, leaders increasingly focus on:
- Whether current architectures can support continuous intelligence
- How AI decisions are governed, explained, and audited
- Where responsibility sits when systems act autonomously
- How to balance innovation speed with operational and regulatory risk
The greatest risk is not using AI - it is using it without sufficient control.
Looking Ahead
By the end of 2027, IT, Data & Security functions are no longer background services. They form the control plane of the AI-enabled enterprise.
Organisations that align early enable safe innovation, faster decision-making, and sustained trust. Those that delay will find that AI adoption outpaces their ability to govern it - with increasing operational and regulatory consequences.
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